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Network-based metrics of resilience and ecological memory in lake ecosystems

David I. Armstrong McKay, James G. Dyke, C. Patrick Doncaster, John A. Dearing, Rong Wang
doi: https://doi.org/10.1101/810762
David I. Armstrong McKay
1Geography and Environmental Science, University of Southampton, Southampton, UK, SO17 1BJ (work started here)
2Stockholm Resilience Centre, Stockholm University, SE-10691 Stockholm, Sweden (current address DIAM)
3Bolin Centre for Climate Research, Stockholm University, SE-10691 Stockholm, Sweden
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  • For correspondence: david.armstrongmckay@su.se
James G. Dyke
1Geography and Environmental Science, University of Southampton, Southampton, UK, SO17 1BJ (work started here)
4Global Systems Institute, College of Life and Environmental Sciences, University of Exeter, Exeter, UK (current address JGD)
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C. Patrick Doncaster
5School of Biological Sciences, University of Southampton, Southampton, UK, SO17 1BJ
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John A. Dearing
1Geography and Environmental Science, University of Southampton, Southampton, UK, SO17 1BJ (work started here)
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Rong Wang
6State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, China, 210008
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Abstract

Some ecosystems undergo abrupt transitions to a new regime after passing a tipping point in an exogenous stressor, for example lakes shifting from a clear to turbid ‘eutrophic’ state in response to nutrient-enrichment. Metrics-based resilience indicators have been developed as early warning signals of these shifts but have not always proved reliable indicators. Alternative approaches focus on changes in the composition and structure of an ecosystem, which can require long-term food-web observations that are typically beyond the scope of monitoring. Here we prototype a network-based algorithm for estimating ecosystem resilience, which reconstructs past ecological networks solely from palaeoecological abundance data. Resilience is estimated using local stability analysis, and eco-net energy: a neural network-based proxy for ‘ecological memory’. We test the algorithm on modelled (PCLake+) and empirical (lake Erhai) data. The metrics identify increasing diatom community instability during eutrophication in both cases, with eco-net energy revealing complex eco-memory dynamics. The concept of ecological memory opens a new dimension for understanding ecosystem resilience and regime shifts, with eco-memory potentially increasing ecosystem resilience by allowing past memorised eco-network states to be recovered after disruptions.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Revised manuscript to be submitted to PLOS Computational Biology

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted June 17, 2020.
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Network-based metrics of resilience and ecological memory in lake ecosystems
David I. Armstrong McKay, James G. Dyke, C. Patrick Doncaster, John A. Dearing, Rong Wang
bioRxiv 810762; doi: https://doi.org/10.1101/810762
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Network-based metrics of resilience and ecological memory in lake ecosystems
David I. Armstrong McKay, James G. Dyke, C. Patrick Doncaster, John A. Dearing, Rong Wang
bioRxiv 810762; doi: https://doi.org/10.1101/810762

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